Data lake and self-driven system for operating enterprise and supply chain applications

ABSTRACT

The present invention provides self-driven Artificial Intelligence based system and method for operating one or more applications including enterprise application and supply chain management applications. The system includes centralized data lake for storing data received from plurality of distinct sources, a control tower configured for sensing change in attribute of the received data and determining impact of the change on plurality of functions of EA and SCM applications.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 16/288,513 which was filed with the United States Patent andTrademark Office on Feb. 28, 2019, the entire contents of which isherein incorporated by reference.

BACKGROUND 1. Technical Field

This disclosure relates generally to enterprise applications (EA) andsupply chain management (SCM) applications. More particularly, thisdisclosure relates to self-driven system and methods of operating one ormore applications including enterprise resource planning (ERP) andsupply chain management (SCM) applications.

2. Description of the Prior Art

In any organization, enterprise applications (EA) and supply chainmanagement (SCM) applications plays a very significant role incontrolling various important functions. Such applications includevarious servers, databases, and computer-based systems for managingseveral internal and external aspects of an organization's enterpriselevel and supply chain level data management requirements. Therequirements may be in areas such as finance, sales, logistics, asset,purchasing, and inventory, among others.

The existing enterprise application (EA) including enterprise ResourcePlanning (ERP) application systems that were built to optimize theresources within the walls of an enterprise, present various challenges.Connecting demand with the supply chain in a dynamically changingenvironment within enterprise resources planning is a big problem as theraw data depending on its characteristics and source is stored in silos.Working with such data of different types and characteristics along withother processes including extraction, cleansing of this silos data inreal time is extremely cumbersome requiring technical modifications tothe structural architecture of the existing ERP systems. Further, thesedata silos coupled with multitude of loosely coupled systems preventreal time collaboration among players such as customers, distributors,warehouses, factories, and suppliers.

Further, accuracy and storage of supply chain data flowing throughexisting application systems is a major concern, considering that suchdata gets dirty or outdated extremely quickly. For example, there isdiscrepancy between the entered lead time for suppliers and actual leadtime of suppliers. The same is true for inventory. Further, the suppliernames get duplicated, and a same supplier may exist multiple timeswithin an EA or SCM system. Such data corrupts the entire system asthere are many duplicate items that gives rise to ‘Master DataManagement’ (MDM) problems.

Some of the existing solutions, take the relevant data out of enterpriseapplication system like ERP system for repair and structuring beforeentering it back into the system. However, this approach is not usefuland creates additional problems as the data gets dirty again within afew days. Further, as the volume of data increases it becomes extremelydifficult to identify the section of corrupt data and perform the repairand structuring on the entire data set time and again. In addition, theexercise is very expensive and time consuming. Since, enterpriseapplication system like ERP is inherently a closed system, even additionof a solution on top of the existing ERP system, to resolve the problemfor a short period of time is labor, time and money consuming. Further,such an approach creates problems in the long run as the volume of datakeeps increasing and the complexity of the ERP system makes itimpossible to repair the corrupt data which effects the entire processin ERP management system.

Also, every function in an ERP or SCM system requires specific processor rule to carry out the task. The type and characteristic of datautilized for carrying out these functions is different for differenttasks. Due to inherent structural and architectural limitations, theexisting systems are unable to pre-empt all issues arising out of an ERPor SCM system and devise a solution. Further, the current ERP and SCMsystems are not equipped to handle dynamic changes in the data acrossdifferent modules. Changes to the data is always in silos in the currentsystem and for the change to take effect takes considerable amount ofeffort.

Accordingly, there is a need in the art for improved supply chainmanagement (SCM) and ERP systems that self-evolve and self-drive thefunctions with real-time identification and resolution capabilities.

SUMMARY OF THE INVENTION

Accordingly, this disclosure provides a method for operating one or moreapplications. The method comprises receiving from distinct sources aplurality of data in a data lake, determine characteristic of at leastone attribute of one or more of the plurality of received data whereincharacteristic includes change in the at least one attribute ordetermination of the attribute as a new attribute, in response to changein the at least one attribute, identifying a plurality of data models togenerate an impact data for predicting impact of the change on the oneor more applications wherein the plurality of data models isauto-selected based on the change, and creating at least one script by abot based on the plurality of data models, the change in the at leastone attribute, the impact data and AI based processing logic forrecommending an action/task wherein a plurality of functions of the oneor more applications are re-calibrated automatically in real-time basedon the recommended action/task.

In an embodiment, the present invention provides a self-driven systemfor operating one or more applications. The system includes a data lakeconfigured to store a plurality of data from distinct sources inreal-time, a control tower configured for controlling a plurality offunctions associated with the one or more applications wherein thecontrol tower determines characteristic of at least one attribute of oneor more of the plurality of received data wherein characteristicincludes change in the at least one attribute or determination of theattribute as a new attribute, a controller encoded with instructionsenabling the controller to function as a bot to generate a plurality ofdata models created for performing the plurality of functions byutilizing a library of functions stored on a functional database of thedata lake, and an AI based prediction and recommendation engine coupledto a processor configured for processing at least one predictionalgorithm to generate at least one recommendation option in real time,wherein a bot creates at least one script based on the data models, thechange in the at least one attribute, an impact data and AI basedprocessing logic for recommending an action/task to automaticallyre-calibrate the plurality of functions of the one or more applications.

In another embodiment, the present invention provides a data lake for aself-driven system configured to operate one or more applications. Thedata lake includes a plurality of relational and non-relationaldatabases configured for storing a plurality of structured orunstructured data received from distinct sources in real-time, at leastone functional database storing a library of functions utilized forperforming a plurality of functions of the one or more applicationswherein a plurality of data models generated by a controller performsthe functions in real-time, and a plurality of data models databaseconfigured for storing the plurality of data models, wherein the datalake is configured to store re-calibrated or re-modelled data modelsassociated with the one or more applications wherein the data models arere-calibrated based on a predicted impact of a new attribute of thestored data on the one or more applications.

In yet another embodiment, the present invention provides a controltower for a self-driven system configured to operate one or moreapplications. The tower includes a tracking module configured tointeract with a plurality of nodes associated with the one or moreapplications wherein the nodes are configured to interact with eachother for performing a plurality of functions, sensing means for sensingcharacteristics of a data received at a data lake wherein are-calibration of a plurality of data models is triggered based on thesensed characteristics of the received data only in case of enhancedperformance by the models, and an analytics module configured to controlan AI based prediction and recommendation engine wherein the engine iscoupled to a processor configured for processing at least one predictionalgorithm to generate at least one recommendation option in real time,wherein a bot creates at least one script based on the data models, thechange in the at least one attribute, an impact data and AI basedprocessing logic for recommending an action/task to automaticallyre-calibrate the plurality of functions of the one or more applications.

In an embodiment the present invention provides a self-driven system foroperating on one or more applications. The system includes a querylanguage (QL) tool configured for receiving, translating and extractingdata related to a plurality of functions of the one or moreapplications. The tool includes an electronic user interface configuredto receive a query data from the user, a translator/interpreter fortranslating the query data into characters using natural languageprocessing (NLP) and generating a plurality of tokens, a code generatorconfigured to receive the tokens from the translator/interpreter andgenerating a code using a data mapper and ingestion module for creatingan AI based machine learning query, and at least one data model createdbased on at least one attribute of the query data and the tokens,wherein the machine learning query is processed to extract arecommendation based on to the query data received from the user,wherein a bot creates at least one script based on the data models, themachine learning query, the at least one attribute of the query data andAI based processing logic for recommending an action/task toautomatically re-calibrate the plurality of functions of the one or moreapplications.

The present invention provides several advantages over the prior art.For example, in one advantageous aspect, the present invention providesa self-driven ERP or SCM system and a method for operating the same withfaster processing times, reduced error and accurate data flow across theplatform. The system utilizes sub network of devices and server forsecured communication with reduced processing time due to automaticcreation of scripts by a bot based on the data models, the change in theat least one attribute, the impact data and AI based processing logicfor recommending an action/task to a user. The system includesre-calibration of a plurality of functions of the one or moreapplications in real-time based on the recommended action/task foraccurate results. All of this results in a significant improvement inthe overall functioning of an enterprise and supply chain computersystem.

In an advantageous aspect, the system and method of the presentinvention optimizes resources by considering the customer demand andsupply market conditions in real time. The system is driven byinterconnected data across the entire supply chain.

In an advantageous aspect, the system of the present invention is notlimited to a single set of rules, logic and workflow. The rules, logic,workflow change by industry, geography, commodity etc. to transform datamodels for achieving self-driven system capable of producing accurate,faster and efficient data results.

The invention provides a self-driven ERP system that is not dependent onsingle set of machine learning or AI algorithms or certain data sets.These algorithms or data sets change, evolve over time and the system isconfigured to use these algorithms and data sets and thus continue toimprove its predictive capability.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be better understood and when consideration is givento the drawings and the detailed description which follows. Suchdescription makes reference to the annexed drawings wherein:

FIG. 1 is a view of AI based self-driven system for operation of one ormore applications including EA and SCM in accordance with an embodimentof the invention.

FIG. 1A is a perspective view of a high-level architecture of aself-driven system for one or more applications including EA and SCM inaccordance with an embodiment of the invention.

FIG. 1B is a perspective view of the system layers in accordance with anembodiment of the invention.

FIG. 1C is a data platform for the self-driven system in accordance withan embodiment of the invention.

FIG. 1D is a block diagram of a query language tool for the self-drivensystem in accordance with an embodiment of the invention.

FIG. 1E is a block diagram for an event flow in the self-driven systemin accordance with an embodiment of the invention.

FIG. 1F is a block diagram of a recommendation platform generatingrecommendation of a task/action to a user of the self-driven system inaccordance with an embodiment of the invention.

FIG. 2 is a flowchart depicting a AI based method for operating one ormore application in accordance with an embodiment of the invention.

FIG. 3 is a flowchart depicting an example embodiment with supplier dataand AI based remodeling and re-calibration of data models and functionof one or more application including EA and SCM in accordance with anembodiment of the invention.

FIG. 3A is a view of a flow diagram depicting a self-driven auto trainedmachine learning in EA and SCM applications in accordance with anembodiment of the invention

FIG. 4 is view of a flow diagram depicting as an example a real-timedata cleansing and de-duplication in EA and SCM applications for itemdata in accordance with an embodiment of the invention

FIG. 5 is a view of is a flowchart depicting a method of operating onERP and SCM applications with integrated functions and sub-networkcomponents in accordance with an embodiment of the invention.

DETAILED DESCRIPTION

Described herein are nonlimiting example embodiments of the presentinvention, which includes Artificial Intelligence, machine learningbased self-evolving ERP systems and methods for operating the same.

The various embodiments including the example embodiments will now bedescribed more fully with reference to the accompanying drawings, inwhich the various embodiments of the invention are shown. The inventionmay, however, be embodied in different forms and should not be construedas limited to the embodiments set forth herein. Rather, theseembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of the invention to thoseskilled in the art. In the drawings, the sizes of components may beexaggerated for clarity.

It will be understood that when an element or layer is referred to asbeing “on,” “connected to,” or “coupled to” another element or layer, itcan be directly on, connected to, or coupled to the other element orlayer or intervening elements or layers that may be present. As usedherein, the term “and/or” includes any and all combinations of one ormore of the associated listed items.

Spatially relative terms, such as “data,” “characteristics,” or“attributes,” and the like, may be used herein for ease of descriptionto describe one element or feature's relationship to another element(s)or feature(s) as illustrated in the figures. It will be understood thatthe spatially relative terms are intended to encompass differentorientations of the structure in use or operation in addition to theorientation depicted in the figures.

The subject matter of various embodiments, as disclosed herein, isdescribed with specificity to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different features orcombinations of features similar to the ones described in this document,in conjunction with other technologies. Generally, the variousembodiments including the example embodiments relate to self-driven ERPsystem and methods of operating the same.

Referring to FIG. 1 , a self-driven system 100 for operating one or moreapplications including supply chain management (SCM) and enterpriseresource planning (ERP) applications is provided in accordance with anembodiment of the present invention. The system 100 includes at leastone computing device/entity machine 101 for initiating at least onefunction to be performed on the one or more applications over a network.The system 100 further includes a server 106 configured to receive inputfrom the entity machine 101. The system 100 includes a supportarchitecture 107 for performing the functions on the one or moreapplications depending upon the type of input received at the server106. The system 100 includes a data lake 108 for storing plurality ofdata from distinct sources, where the data includes, text data, voicedata, image data, functional data, data models, scripts etc. to beprocessed based on Artificial intelligence and machine learning. Thesystem 100 connecting various elements through a network 109. Thenetwork 109 enables formation of sub networks depending on therequirement of the function to be performed on the application.

In an exemplary embodiment, the self-driven system 100 of the presentinvention operates one or more applications that may include enterpriseapplications (EA) and/or supply chain management (SCM) applications.

In an embodiment, the enterprise applications include financeapplications like automated billing applications and payment processingapplications, Customer relationship management applications (CRM) andenterprise resource planning applications (ERP).

In an embodiment, the recommended task/action includes auto adjust datafor the plurality of functions, risk mitigation, removing duplicateentry, or direct interaction with the plurality of nodes. Further, theduplicate entry can be of any data existing in the EA and SCMapplications, including but not limited to supplier, invoice, contractetc.

In an embodiment, the entity machine 101 may communicate with the server106 wirelessly through communication interface, which may includedigital signal processing circuitry. Also, the entity machine 101 may beimplemented in a number of different forms, for example, as asmartphone, computer, personal digital assistant, or other similardevices. The entity machine 101 includes internal circuitry 102 that mayinclude processor 103, memory 104 and storage device 105.

In an embodiment the server 106 of the invention may include varioussub-servers for communicating and processing data across the network.The sub-servers include, but are not limited to, content managementserver, application server, directory server, database server, mobileinformation server and real-time communication server.

In an example embodiment, the server 106 may include electroniccircuitry 110 for enabling execution of various steps by a processor ofthe server 106. The electronic circuity 110 has various elementsincluding but not limited to a plurality of arithmetic logic units (ALU)111 and floating-point Units (FPU) 112. The ALU 111 enables processingof binary integers to assist in formation of a tables/matrix ofvariables where a script created by data models is applied to data setsimpacting multiple functions like demand planning, supply planning,forecasting, budgeting etc. in applications like ERP or supply chainmanagement (SCM). In an example embodiment, the server electroniccircuitry 110 as shown in FIG. 1 , may additionally include otherprocessors, memory, storage devices, high-speed interfaces connectedthrough buses for connecting to memory and high-speed expansion ports,and a low speed interface connecting to low speed bus and storagedevice. Each of the components of the electronic circuitry 110 areinterconnected using various busses and may be mounted on a commonmotherboard or in other manners as appropriate. The processor 114 canprocess instructions for execution within the server 106, includinginstructions stored in the elements of the data lake 108 like memory oron the storage devices to display graphical information for a GUI on anexternal input/output device, such as display coupled to a high-speedinterface. In other implementations, multiple processors and/or multiplebusses may be used, as appropriate, along with multiple memories andtypes of memory. Also, multiple servers may be connected, with eachserver providing portions of the necessary operations (e.g., as a serverbank, a group of blade servers, or a multi-processor system).

In an example embodiment, the support architecture 107 of the system 100includes a controller 113 encoded with instructions enabling thecontroller 113 to function as a bot configured to generate plurality ofdata models for performing multiple functions. The controller 113selects an Artificial Intelligence based dynamic processing logic usingthe bot to reduce the processing time for performing multiple functionsof the ERP or SCM applications. The processing logic for each functionof an application is different. The controller 113 is configured todetermine and generate a processing logic for each function in real-timedepending on the received data and processing cycle of that receiveddata in the one or more application. For eg., the processing logic mayinclude serial or parallel processing of certain functions depending onthe impact on the one or more applications. The system also includes aprocessor 114 configured to process various functions based on the AIbased processing of data sets and data models by the bot. The supportarchitecture 107 includes a data manager 115 for managing data relatingto any function of the Enterprise application EA or SCM application. Inan example embodiment, the data may include supplier data with changedattributes like lead time during inventory or transportation function ofa supply chain application. The support architecture 107 includes an AIengine 120 for determining relevant data models stored in a data modeldatabase 126 and created by a generation mechanism 120 a for executionusing the bot based on data sets received at the data lake 108. Further,the support architecture 107 includes a data cleansing and normalizationengine 116 for receiving processed and cleansed data sets from thefront-end server 106 to execute multiple functions of the one or moreERP and SCM application. The support architecture further includes acontrol tower 117 for controlling a plurality of functions associatedwith the one or more applications wherein the control tower determinescharacteristic of at least one attribute of one or more of the pluralityof received data wherein characteristic includes change in the at leastone attribute or determination of the attribute as a new attribute. Thecontrol tower 117 includes a tracking module 118 configured to interactwith a plurality of nodes associated with the one or more applicationswhere, the nodes are configured to interact with each other forperforming a plurality of functions. The control tower 117 also includesa sensing means 119 for sensing characteristics of a data received at adata lake. The sensing means 119 of the support architecture 107triggers a re-calibration of the plurality of data models based on thesensed characteristics of the received data only in case of enhancedperformance by the models. The control tower includes an analyticsmodule 117 a configured to control the AI based prediction andrecommendation engine 120.

In one embodiment, the support architecture 107 may include imageprocessing unit, for processing an image data and converting it to atext data. Also, the sensing means 119 of the architecture 107 mayinclude a voice to text convertor for enabling faster and accurateconversion of voice data to text data for execution of multiplefunctions. Further, the support architecture 107 includes a verificationengine 121 for verifying the received data after matching with existingdata in the data lake 108 to determine using control tower 117, thecharacteristic of the data including any change in attribute of the dataor receipt of a new attribute data.

The processor 114 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 114may provide coordination of the other components, such as controllinguser interfaces, applications run by devices, and wireless communicationby devices.

The Processor 114 may communicate with a user through control interfaceand display interface coupled to a display. The display may be, forexample, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or anOLED (Organic Light Emitting Diode) display, or other appropriatedisplay technology. The display interface may comprise appropriatecircuitry for driving the display to present graphical and otherinformation to an entity/user. The control interface may receivecommands from a user and convert them for submission to the processor114. In addition, an external interface may be provided in communicationwith processor 114, so as to enable near area communication of devicewith other devices. External interface may provide, for example, forwired communication in some implementations, or for wirelesscommunication in other implementations, and multiple interfaces may alsobe used.

In an embodiment, the data cleansing and normalization engine 116 isconfigured to clean data received at the data lake in real time usingnatural language processing and machine learning algorithms for enhancedaccuracy. Since, the data will be received from multiple disconnectedsources, the engine 116 has an ability to remove duplicates, standardizeand group the data. The cleansing engine is coupled to a data mapper andcurator engine. The engine 116 detects and corrects Corrupt or duplicateor vague data. Further, the cleansed data is sent for approval through arouting mechanism post which they are stored in master data tables ofthe data lake. Also, an audit of the received data and cleansed data isstored in the data lake.

In an example embodiment, the data lake 108 includes plurality ofdatabases as shown in FIG. 1 . The data lake 108 includes a relationaldatabase 122 a for storing related data sets received from distinctsources, a non-relational database 122 b for storing non-related rawdata sets, a functional database 124 for storing a library of functionsenabling creation of a plurality of data models for execution of tasksin one or more applications including ERP and SCM, a plurality ofregisters 125 for temporarily storing data from various sources fordetermination of characteristic of the data like change in attribute ofreceived data or receipt of a new attribute data itself. The receiveddata may be image data, voice data or text data where the image andvoice data can be converted to text data for analysis. The data lake 108further includes a data model database 126 for storing plurality of datamodels, where the data models are re-calibrated based on a predictedimpact of a new attribute data of the stored data on the one or moreapplications.

The data lake 108 may be supported by different memory like a volatilememory, a non-volatile memory or memory that may also be another form ofcomputer-readable medium, such as a magnetic or optical disk. The memorymay also include one or more storage devices capable of providing massstorage. In one implementation, at least one of the storage devices maybe or contain a computer-readable medium, such as a floppy disk device,a hard disk device, an optical disk device, a tape device, a flashmemory or other similar solid-state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations.

Referring to FIG. 1 , the various elements like the support architecture107 and the memory data lake 108 are shown as external connections tothe server 106 in accordance with an embodiment of the invention.However, it shall be apparent to a person skilled in the art that theseelements may be part of an integrated server system. Also, some of thesub-elements of the support architecture 107 and the data lake 107either alone or in various combinations may be part of a server systemas other external connections.

In an embodiment the tracking module 118 of the system 100 is an IOTdevice or smart device configured to capture, store and transmit a datarelevant to the one or more applications. The IOT device may be insecured communication with the at least one server 106 as part of thesub network.

In an example embodiment, the at least one IOT device may be a trackingdevice, an intelligent sensor, a smartphone, a voice controller, animage capturing device, a gesture controller, a smart watch or acombination thereof. The IOT device may include sensor processors withinternal circuitry that may include processor, memory and storagedevice. The IOT device data includes sensor data on plant machinery,logistics carriers, security systems, warehouse cameras and sensors etc.

Referring to FIG. 1A a perspective view of a high-level architecture(100A) of a self-driven system for one or more applications including EAand SCM is shown in accordance with an embodiment of the invention. Thehigh-level system architecture includes a user interface (UI), anapplication programming interface (API), functional objects, data accessobjects, an event handler, and the data lake 108. The UI interacts withthe data lake through a master data API. The data lake 108 includes afile store 123 a, a cache 123 b, a graph store 123 c in addition to therelation database 122 a and non-relational database 122 b as shown inFIG. 1A.

Referring to FIG. 1B and 1C, system layer architecture diagrams withdata lake/platform (100B, 100 c) of AI based self-driven ERP and SCMsystem is shown in accordance with an embodiment of the presentinvention. The system 100 a includes a plurality of distinct data sourcelayer 127 to capture all customer, factory, supplier, machine andthird-party sources of data (both structured and unstructured), the datalake layer 108 storing all data received from the distinct data sourcelayer 127, an application function layer 128 configured to re-calibratefunctions based on data models and scripts generated by a bot. The datamodels are auto-generated based on change in attribute of the receiveddata to determine the impact of the change on the functions of the oneor more applications. The system includes the control tower 117configured to control the entire demand to supply application.

The system layer architecture includes an application/dashboard layer129, a Query language tool (QL) 130, data governance &standardization/protocol layer 131, a mapper and ingestion module 132 a,a data curator 132 b, event stream/IOT stream/Queue 133, and an APImanagement gateway 134. The distinct data source layer 127 includesexternal source 127 a, internal source 127 b and IOT source 127 c.

In an exemplary embodiment, Query Language (QL) tool 130 provides aflexible and powerful way to get insights on transactional view acrosssupply chain data model. The QL tool provides ability to apply desiredmachine learning algorithm on key attributes from the data platform. Therecommendation is attached to desired workflow/UIelement/rules/validations. Also, custom query is built to get access tooperation store in real-time. The simplicity of QL tool allowsnon-technical stakeholders to drive optimal outcome of process bytweaking the operational parameters from control tower 117. The desiredoutput is available in the form on simulation before it is applied toactual workflows.

In an embodiment, the query language tool (QL) 130 includes componentstructure 100 c as shown in FIG. 1C. The structure is broken into 4layers viz, simulator UI 130 a, Translator 130 b, Code Generator 130 cand model 130 d. The simulation UI 130 a enables user to draftstatements/query as per underlined model provided through intelligentsensing. The Translator 130 uses NLP and domain specific nomenclaturerepository, to tokenize query string received from user. Tokenizer takesa sequence of characters and output a sequence of tokens. It willanalyze character by character, using multiple levels of lookahead inorder to identity what token is currently being examine. The CodeGenerator 130 c extracts Keywords and tokens that are used to generateunderlying Machine Learning query and big data query. The Mapper isresponsible to generate code and the model 130 d utilized domainattributes, Synonyms and tokens.

In an embodiment, the tool includes an AI based prediction andrecommendation engine coupled to a processor configured for processingat least one prediction algorithm to generate at least onerecommendation option/task/action in real time.

In an embodiment, the tool is configured to attach the recommendedtask/action to a desired workflow or User interface element or set ofrules or validations.

In an embodiment, the tool is configured to generate custom query foraccessing data lake in real-time.

In an exemplary embodiment, the data lake 108 of the system is designedon “Data First strategy”. Procurement and Supply Chain industry has beentackling capacity challenge, primarily due to digitalization and accessto real-time time feed from manufacturing unit, logistic applications,including sensors and IOT. It is designed to handle wider variety ofdata from different source systems and starts with paradigm shift ofjust in time data warehouse with real-time integration and ML models.Platform will have capability to introduce new model on the enterprisedata warehouse. Access to data quickly will help users in analysis.Users will have access to data from multiple sources in differentformats.

Referring to FIG. 1B & 1C, Data lake 108 also comprises of the graphstore 123 c which enables providing real-time recommendation based onhistorical data of demand and supply. It also provides ability for endusers to track life cycle and relation of entities in the system. DataRelation analytics (using Graph store) will help users viewrelation-first perspective of their data which is not possible inclassical data model. Information will feed into Analytics and Dashboard129, with a view getting mode insights. Graph algorithms library willalso provide the ability to detect hard-to-find or complex patterns andstructures in supply chain data model. The graph store creates ahierarchical tree of relations based on user actions. Further it enablesQL tool to search results efficiently.

In another exemplary embodiment, the data Curator engine 132 b is one ofthe key subscribers to application events. Curator engine 132 bsubscribes to all incoming messages from service provider includingexternal sources 127 a, internal sources 127 b and sensors/IOT devices127 c. It collects data from diverse sources, acts a gateway andidentifies data attributes to be extracted from application event. Thecurator engine 132 b with the help of mapper and ingestion module 132 astores the received data in multiple type stores viz, the search storefor advance search, graph for data and relations, flat structure forlogs purpose etc. The Curation including selection and organization ofdata takes place through capturing metadata and lineage and making itavailable in a data catalog.

The data flows in the data lake in real-time processing through eventstream layer. Domain Model exposed through the query language (QL) tool130 enables user to self-serve their data and analytical requirements.Models developed by users are utilized to improve the insights forfuture purpose.

In an embodiment, the system architecture and applications are designedon API first strategy. All producers and consumers of the data in thesystem expose data using restful services (API). Central API gateway 134ensures high reliability and fault tolerance. In case callingapplication is not reachable, it includes inbuilt retry policies tomanage connectivity failures. Further, it provides additional layer ofsecurity.

In an exemplary embodiment, the system data platform enables eventstream/IOT stream layer 133. The applications in supply chain emitsapplication events based on automated workflow or user-based actions.The data received from various sources including sensors and IOT devicesare routed through event handler/integrator. The integrator component isresponsible to establish connect with event store and push message.Message router consumes message from store and republishes it tosubscribers as per routing rules and message type. The subscriber willcomplete the desired functional workflow. The strategy assists inremoval of peer connection for scaling the applications. Layer isresponsible to provide real time feed to the data lake, which in turnwill trigger model and classification engine.

In an embodiment the data Mapper layer and ingestion module 132 a isresponsible to transform data received from disconnected source systems.Ingestion pipeline and sophisticated AI based Data Mapper pushes data inlarge repository.

In an exemplary embodiment the control tower 117 is configured for realtime visualization of flows in the one or more applications, switchingbetween data models, setting up alert-notifications, data analytics,ensuring security of the data. wherein the tower enables easycommunication between the nodes as well as allows having visibility andmanages the plurality of functions across the one or more applications.

In an embodiment, the received data includes attributes of differenttypes including alphabets, numbers, images, wherein the tower 117 isconfigured for controlling application of multiple data models on thereceived data including switching between data models in real-time.

In an example embodiment, the Data Lake 108 includes data received fromnodes or sources such as customers or retailers, distributors,factories, productions, suppliers etc. It also includes data fromoutside sources such as financial markets, weather, social media,geo-economics etc. On this Data Lake the executional platform is builtthat includes functions or products such as planning, Production,Procurement, Suppliers etc. This enables the system to build a real timemachine learning or AI based recommendations that guide the user toconduct his or her work on daily basis with more accurate data, withhigher confidence and from a system that is easier to use andintelligent.

In an embodiment, the plurality of distinct data sources includesinternet of things (IOT), demand from various sources at differentlevels like retailers, distribution channels, POS systems, customerfeedback, supplier collaboration platform, invoices, purchase orders(PO), finance modules, inventory management module, contracts and RFxmodule, supplier module, item master, bill of materials, vendor master,warehouse management module, logistics management module, social media,weather, real time commodity and stock market prices, geo-political newsetc. It shall be apparent to a person skilled in the art that the datasource may include other source within the scope of the presentinvention.

In an embodiment, the system of the present invention includes means toadd incremental data sources as the system evolves.

In an example embodiment, the EA and SCM applications include aplurality of nodes at the data source layer 127 like inventory,logistics, warehouse, procurement, customers, supplier, retailers,distributors, resellers, co-packers and transportation wherein the nodesinteract with each other to structure the plurality of functionsassociated with the applications. It shall be apparent to a personskilled in the art that the nodes may include other nodes within thescope of the present invention.

In an embodiment, the plurality of functions of the application functionlayer may include demand planning, supply planning, production planning,forecasting, smart factory and fulfillment planning among others. Itshall be apparent to a person skilled in the art that the EA and SCMapplications may include other functions within the scope of the presentinvention.

In an exemplary embodiment, the system of the present invention includesinterconnected data across the plurality of functions connecting demandwith supply by combining customer and supplier data in real time andthereby structuring a collaborative platform for multiple entities likesupplier, customer, factories and warehouses. Since data related to eachfunction is stored and managed through a common data lake, the data isinterconnected across functions through the common platform. Demand andsupply is connected through the collaborative platform as the impact ofchange in data is assessed for individual functions using analysis ofthe same received data on the common platform. Also, the relation andutilization of all data sets across functions and applications isassessed to connect demand and supply.

In an embodiment, the collaborative platform is configured to maximizeprofits and margins as an accurate demand and commodity prices data isavailable in real-time for improved decision making based on therecommended tasks.

In an embodiment, the impact data is determined by the data models forpredicting impact of the change on the one or more applications whereinthe data models are auto-selected based on the change. Consider,transportation in SCM, if a supplier address is changed in the receiveddata, then it would impact multiple applications and functions. For eg.,the lead time would change based on distance of the supplier from thedelivery point, the pricing of the service would change which in turnwould impact functions like PO etc. This change in data or attribute ofthe received data would impact multiple functions across one or moreapplications at a different scale. The impact data determined by thedata models provides a common reference for assessing the impact of thechange thereby enabling the bot to structure the multiple functions inreduced time frame for faster processing.

In an embodiment, the system is provided in a cloud or cloud-basedcomputing environment.

In an embodiment the system 100 includes a test module configured totest the plurality of data models and apply the tested data models tothe one or more applications in real time. The testing is done viasimulation on the control tower. Further, real time data is fed into thedata model and results are compared in real time to determine accuracyand efficiency of the data models. The system 100 is configured forswitching data models for different applications based on the functionsand efficiency of the tested data models for those functions. Theswitching happens in real time and is an extremely complex processcarried out using AI based analysis of performance of the data models,the received data, and the functions related to different applicationsof ERP and SCM system.

In an example embodiment of the invention, the self-driven ERP and SCMsystem 100 provides a sub network including the at least one server 106in communication with a plurality of distinct data sources including TOTdevices (examples of tracking module 118) like image capturing device,smartphone and sensor. Further, the sub network includes communicationbetween various network components like sub-servers, TOT devicesassociated with multiple ERP and SCM applications, for executingidentified functions. The sub network enables interlinking of change indata to different functions for performing an integrated operation andsimultaneously also enables performance of identified single function byutilizing essential steps from the other network components of thesub-network.

In an embodiment the ERP and SCM applications include suppliermanagement operations, procurement operations, inventory managementoperations, account payable operations etc. An example of the presentinvention organizes the supply chain between manufacturers and serviceproviders. In an example of the present invention, the SCM operationsinclude elements that enable management of end-to-end supply chaininformation such as demand planning, order fulfillment, scheduling,inventory, etc.

In an example embodiment of the present invention, SCM with multiplemanufacturers and service providers, some of the advantages of thepresent system include the fact that economies of scale are enabled,procurement and inventory are rationalized, distribution and logisticsfacilities are rationalized, and the development of an industry-widestandard is facilitated.

In an embodiment, the demand and supply of manufacturer offerings areplanned utilizing the sub network in operation and orders for themanufacturer offerings are also managed utilizing the sub network withserver and IOT devices in communication with each other for datacapturing and exchange. The sub network is also utilized to manage subnetwork assets including providing maintenance and service for the subnetwork assets utilizing the sub network.

In one of the advantageous aspects of the present invention, theself-driven system for ERP and SCM applications provides revenueenhancement, cost reduction and capital reduction by efficientutilization of resources with reduced timelines due to real-timeremodeling or recalibration of data models/machine learning modelsimpacting multiple functions across ERP and SCM.

In a related aspect, the revenue enhancement includes faster siteintegration time, enhanced network performance, rapid integration ofacquisition and faster order to cash. The cost reduction includesduplication reduction, rationalization of distribution facility,rationalization of procurement operations, simplified processes andrationalization of transportation. The capital reduction includesreduced inventories due to faster processing times of SCM operations,and appropriate utilization of manufacturing capacity.

In an embodiment, the inventory management function of the SCMapplication at a warehousing includes scanning of a set of receivedgoods by a tracking module/IOT device 118 and transmitting the specificwarehousing data of the scanned goods to the at least one server forstoring in the data lake. Any change in the data is analyzed andreflected across functions using data models and scripts.

In example embodiments, the bot is a software bot or a combination of asoftware and hardware bot. In an embodiment, the software bot is acomputer program enabling a processor to perform remodeling orrecalibration of functions by utilizing AI.

In another embodiment, the bot as a combination of hardware andsoftware, where the hardware includes memory, processor, controller andother associated chipsets especially dedicated to perform recalibrationof data models to carry out functions for ERP and SCM applications.

In an embodiment, the at least one server includes a front end webserver communicatively coupled to at least one SQL server wherein thefront end web server is configured for reprocessing the functions of theone or more applications based on the plurality of data models andscript by receiving the recalibrated function processed by the SQLserver and applying the AI based dynamic processing logic to the datamodels and functions using the bot. The AI based processing logicincludes a sequential, a parallel or a switching-based processing logicor a combination thereof.

In an embodiment, the system 100 includes an execution engine forreceiving changed data and generating impact data processed from thefront-end web server for determining impact of change on plurality offunctions of the one or more applications to predict and recommend atask/action to the user for enabling the user to initiate the actionthrough the electronic user interface.

Referring to FIG. 1E a block diagram 100E of the self-driven system withservice provider structure 135 and subscriber structure 136 is shown inaccordance with an embodiment of the invention. The interaction and dataexchange between the service provide and subscriber is through the APIgateway 133, event management block 134 and routers 137.

Referring to FIG. 1F is a block diagram 100F of a recommendationplatform generating recommendation of a task/action to a user of theself-driven system is shown in accordance with an embodiment of theinvention. The service provider structure 135 interacts through eventstream 134 with data lake having graph store 123 c and search store 123d. The data extracted from the data lake after NLP of the received dataand using data frame SQL is provided to the subscriber through the userinterface.

Referring to FIG. 2 a flowchart 200 depicting a method for operating oneor more applications including ERP and SCM applications is shown. Themethod comprises the steps of S201 receiving from distinct sources aplurality of data in a data lake. In S202 determine characteristic of atleast one attribute of one or more of the plurality of received data. InS203 checking if received data is new data or data with new attribute.If No, then in S204 checking if there is change in received data orattribute of received data. If No, then in S205, no data remodeling orrecalibration required. If there is change in data or attribute ofreceived data, then in S206 in response to change in the at least oneattribute and/or change in the data, generating by data models an impactdata for predicting impact of the change on the one or more applicationswherein the data models are auto-selected based on the change. In S207creating at least one script by a bot based on the data models, thechange in the at least one attribute, the impact data and AI basedprocessing logic for recommending an action/task wherein a plurality offunctions of the one or more applications are re-calibratedautomatically in real-time based on the recommended action/task. In S203if it is determined that that received data is a new data or data withnew attribute then in S208 in response to determination of the attributeas a new attribute, determining the one or more applications utilizingthe received data from the distinct sources; re-calibrating orremodeling the data models associated with the one or more applicationsbased on the new attribute wherein the data models are re-calibratedbased on predicted impact of the new attribute on the one or moreapplications wherein an AI based recommendation engine is configured torecommend the action/tasks.

In one example embodiment as depicted from the flowchart 300, in S301 asupplier data is received at the data lake and the impact of the datafor carrying out a method of operating on one or more applications. InS302, determining characteristic of at least one attribute of thesupplier data amongst (Vendor name, firmographic attributes such asCity, address, operating countries, number of employees, financials,products or services offered etc) or other attributes of the suppliersuch as average lead time for delivery etc, from the received supplierdata. This data may change in any of the modules/functions of the one ormore applications. Since, it extremely difficult to track this change inany other module of the application, it leads to delayed actions. Theself-driven system and method of operating on ERP and SCM application ofthe present invention enables, reflection of any change in any of theseattributes across other modules/functions. Further, the change is alsoconsidered in the ML (machine learning) models driving specific actionsin real-time. In case the supplier is a critical supplier who suppliesspecific materials critical to the manufacturing line operations, anychange would be very critical. If the system senses that the deliveriesof this supplier have been consistently delayed over the last 3-4 cyclesand these changes are used by the data models or machine learningalgorithms to determine the new lead time for the specific products,then this insight is extremely valuable to the organization in many waysto take action or perform tasks based on recommendation. In such ascenario, the action includes initiating a call/meeting with supplier tounderstand delays, take immediate corrective actions to mitigate therisks, adjust the process with refreshed lead times or adjust inventorywith new forecasts. The method steps for operating on the one or moreapplications include S303, checking if new attributes are introduced todata lake, if yes then in S304 Cleansing/transformation of newattributes (remove outliers, normalization, impute missing,dimensionality reduction etc). In S305, a correlation between newattributes and existing model predictor attributes is checked. In S306,highly correlated variables are removed. The correlation betweenattributes/variables are measured by techniques including but notlimited to correlation co-efficient, variable inflation factor (VIF)etc. In S307, retraining each model with existing and new attributes. InS308, checking if metrics are provided, if no then in S309, existingmodel is retained else, if yes then in S310, new model is deployed viadocket imager. In S303 if new attributes are not received then checkingif there is any drastic change in value of attribute of the receiveddata in S311. The change is a preset threshold change in the value orpercentage of change as set by the user. If there is drastic changethen, in S312 alternate model is deployed. If no drastic change then, inS313 checking in real-time if alternate models in library/data modeldatabase are performing better. If Yes then, alternate model isdeployed, else if no, then in S314 data model with new attributes withlarger data variance is retrained. In S315, results with differentdatasets are tested and validated. In S316, retrained model is deployed.When the data is received at the data lake, at least one data model isidentified from the data model database for generating impact data.Also, simultaneously the performance of the identified data model isdetermined before applying to the received data based on historicalrecords of the data model. In S311 a, checking if there is drasticreduction in model performance (less than threshold). If no, then S312 anothing is done, else in S313 a checking if alternate models in thelibrary/data model database are performing better. If yes, then in S314a new model is deployed else, in S315 a model with more attributes,better sampling techniques is retrained. In S316 a, alternate model isdeployed.

In exemplary embodiment, each of the plurality of machine learning (ML)models are built on real time data across all data points in the supplychain data lake, with multiple predictor attribute. This leads to modelswith higher degree of accuracy and confidence.

In another embodiment, any changes in the data in any of modules of theERP system is sensed by the control tower and the real time dashboardsand an auto-refresh/Auto-training of the ML models is triggered asdepicted in flow diagram 300A of FIG. 3A. In an exemplary aspect, theprocess is set such that the new model will be evaluated with thecurrent model and will replace the current model only if it outperformsthe current model.

In yet another embodiment, if there are any new data fields from newsources of data being added to the data lake, auto-ML will be triggeredto conduct the entire process of exploratory data analysis, modelbuilding and deployment.

In an exemplary embodiment, the system and method of the presentinvention provides multiple ML models (different techniques) for thesame use case scenario. At any point in time, there's one model that isoperational or deployed for a given use case scenario, but the othermodels are constantly being refreshed and monitored for better metricsof performance. The models can be switched through the control Panelmanually or automatically. This overcomes the issues related to theconcept drift or the decreased performance of ML models over a period.

In an embodiment, the system includes pro-active detection algorithmsfor any record/transactions (items/Suppliers/PO/Invoices etc) beingentered by a user (supplier/Customer/Employee etc) at the userinterface. These will ensure that the Master tables are clean, accurate,complete and non-fraudulent/non-duplicate at any point in time and thedata flowing through every single module or pipeline is clean andaccurate. The master tables are stored in relational database 122 a.

In an exemplary embodiment, the data models such as item master cleanserand vendor master cleansing algorithms run at the backend at frequentintervals when it gets triggered with ingestion of new items. If theitems are duplicates with a very high degree of confidence, the systemis sent for approval to the approver immediately. This ensures a highthroughput of the platform maintaining a high level of hygiene andcleanliness. All this is achieved automatically using the bots, datamodels and scripts.

In an embodiment of the present invention, any ERP or SCM operationrequires a finite amount of processing time on a computer processor.Further, the accuracy of results or any process depends on how fasterdata cleansing is carried out in any application. The present inventionrestrains the process of remodeling or re-calibration of data models forcarrying out multiple functions of one or more applications, where thedata model and scripts created by the bot and AI enable real timeremodeling and re-calibration by selecting the fastest processing routefor determining changes in data received at the data lake, whilesimultaneously satisfying the needs of obtaining accurate results, dataelements are organized/processed depending on the demands of thecomputing resources, which allow more functions of the one or moreapplications to be processed with same resources (e.g., disk space,processor speed, memory, etc.). For Eg., a data received with change insupplier lead time would impact inventory, warehousing, transportationfunctions. The real time remodeling of data models to incorporate impactof the change across multiple functions and applications is carried outusing the same resource (data lake, control tower, processor,controller). Thus, the net result of the claimed invention providesimproved processing and functioning of self-driven ERP and supply chainsystems. The logical processes involved with the self-driven ERP and SCMsystem define the improvement.

Referring to FIG. 4 , a flow diagram 400 depicting a data cleaning andde-duplication process for an item data in the one or more applicationsis shown in accordance with an example embodiment of the presentinvention. The self-driven system and method of the present inventionincludes proactive duplicate arrestor 401 configured for real timesearch 402 capabilities initiated by a user through a user interface.The system recommends similar items based on feedback from itemrecommender 403, which in turn processes the search through an itemmaster 404. The item master 404 is connected to the knowledge repository405 interacting with various master databases including data modeldatabases. The system deploys backend data cleaners configured foridentifying and cleansing duplicate item records 406 based on itemdescription 407, item category 408, item specification 409. The systemalso includes AI driven category finder, NLP based feature extractor andNLP based feature enrichment for data received from a user 410 offline.The NLP based feature enrichment of data enables implementation ofmachine learning algorithm/data models 411 for de duplicating itemmaster database.

Various computing devices referred to as IOT devices the entitymachines, server, processor etc. of the present invention are intendedto represent various forms of digital computers, such as laptops,desktops, workstations, personal digital assistants, and otherappropriate computers. Computing devices of the present inventionfurther intend to represent various forms of mobile devices, such aspersonal digital assistants, cellular telephones, smartphones, and othersimilar computing devices. The components shown here, their connectionsand relationships, and their functions, are meant to be exemplary only,and are not meant to limit implementations of the inventions describedand/or claimed in this disclosure.

In an exemplary embodiment the present invention includes anintelligence platform for supporting the various functions carried outby AI based self-driven systems. The platform collects data fromdifferent sources such as customer data, supplier onboarding data, datafrom social media into a data repository and enables more accurateforecasting, budgeting, Commodity management including pricing,variance, supplier Risk and performance management and other benefits.More particularly, the intelligence platform enables the predictionalgorithm to identify and recommend action/tasks to a user.

In an example embodiment, the complexity involved in execution ofvarious supply chain management (SCM) and ERP operations require systemsand methods that process these operations in reduced timelines withaccuracy. Various operations like procurement, inventory management,distribution and warehousing etc., when executed in an integratedmanner, do not merely perform the functions that each operation wouldperform separately. Also, the changes in attribute of data received atthe data lake are subject to processing by data models to determineimpact data related to impact of the change on the functions of one ormore applications to achieve accuracy of data, reduced error and fasterprocessing times, for example, during ERP operation. Consider, forexample, SCM or ERP with demand planning, supply planning, productionplanning, fulfillment planning, and forecasting as functions in ERP andSCM applications as shown in FIG. 5 . The bot and AI in a non-limitingexample of the present invention creates data models and scripts thatare novel in integrating various steps of these functions by analyzingchanges in data or data attributes received in the data lake to processthe operation faster. Also, determining impact of the change in data onthese functions enables faster processing with accuracy, more so,because combining two or more such functions leads to performance ofanother function i.e the combination of functions contributes towardsperformance of another function. In the above example, depicted by theflowchart 500, in S501 identifying the characteristic of received dataincluding change in data attribute or entire data itself In S502, asub-network is created, and functions associated with ERP and SCMapplications of the sub network are identified. In S503, processing ofthe changed data across the application, and the identified functions aspart of a self- driven ERP and SCM system for faster processing isinitiated. Consider some of the functions as sub processes demandplanning in S504, supply planning in S505, production planning in S506,forecasting in S507 and fulfillment planning in S508. Consider the datareceived at the data lake related to a product or item that movesthrough the ERP and SCM applications. Demand planning S504 allowsdetermination of a demand for the item or product considering variousfactors like customer base, consumption, density of population is ageographic location etc. Supply planning in S505 determines actions tofulfill the requirements created from the demand planning with anobjective to balance supply and demand in manner that achieves desiredobjectives of ERP. Production planning S506 enables computation based onavailability of items and capacities to meet customer demand bybalancing the load on the manufacturing resources after considering thehigh throughput capacity of a plant. Forecasting S507 determinesestimate for demand of item, supply of item, and production of the item.Fulfillment planning S508 ensures receiving of the item, packaging andshipping for eventual fulfillment of the order. Any change incharacteristic of data or attributes related to the item/product willaffect all the processes in different manner. In case, the change is notreflected at any of the functions, it shall lead to error and inaccuracyin that function. In case of combining of the functions like demandplanning and supply planning for fulfillment of order, certain factorsare considered related to characteristic of the item itself. When thesefunctions act independently, the supply planning may not consider thechange in the item characteristic. Also, during the Production planningS506, a user may wish to restrict the material composition of the itembased on the demand of the item S504, thereby saving time onmanufacturing items with undesired material characteristics. Whenfunctioning independently, these functions do not consider the changesin other functions of the SCM and ERP. These functions are integrated toperform another function of fulfillment planning S508. The bot considersall these changes for automatically creating scripts and remodeling orrecalibrating functions recommend actions/tasks to a user. Also, itincreases accuracy and reduces the time required for processing anyfunction or combination thereof.

In an advantageous aspect, the system includes complete EA and SCMcapabilities including Real time demand planning form outside to inside(using external and historical data sources), Production and InventoryPlanning Using demand, production and supplier data sources; Supplyplanning based on connecting real time demand with suppliers,Warehousing planning and optimization of warehouse spaces forproductivity and safety; Logistics planning with full optimizationcapability based on demand, suppler and network route data. The systemhas full operational capability where different users can come andconduct their workflows, approvals and issue work orders, purchaseorders, requisitions, etc. They will also be able to receive invoices,receipt orders from their suppliers (both Tier 1 and Tier II) suppliers.

Exemplary embodiments of the present invention, may be a system, amethod, and/or a computer program product. The computer program productmay include a computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out aspects of the present invention including but not limitedto processing prediction algorithm, determining optimizationcharacteristics based on performance data etc. The media has embodiedtherein, for instance, computer readable program code (instructions) toprovide and facilitate the capabilities of the present disclosure. Thearticle of manufacture (computer program product) can be included as apart of a computer system/computing device or as a separate product.

The computer readable storage medium can retain and store instructionsfor use by an instruction execution device, for example, it can be atangible device. The computer readable storage medium may be, forexample, but is not limited to, an electromagnetic storage device, anelectronic storage device, an optical storage device, a semiconductorstorage device, a magnetic storage device, or any suitable combinationof the foregoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a hard disk, arandom access memory (RAM), a portable computer diskette, a read-onlymemory (ROM), a portable compact disc read-only memory (CD-ROM), anerasable programmable read-only memory (EPROM or Flash memory), adigital versatile disk (DVD), a static random access memory (SRAM), afloppy disk, a memory stick, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the internet, a local area network(LAN), a wide area network (WAN) and/or a wireless network. The networkmay comprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

The foregoing is considered as illustrative only of the principles ofthe disclosure. Further, since numerous modifications and changes willreadily occur to those skilled in the art, it is not desired to limitthe disclosed subject matter to the exact construction and operationshown and described, and accordingly, all suitable modifications andequivalents may be resorted to that which falls within the scope of theappended claims.

1. A data lake for a self-driven system configured to operate one ormore applications, the data lake comprises: a plurality of relationaland non-relational databases configured for storing a plurality ofstructured and unstructured data received from distinct sources inreal-time; at least one functional database storing a library offunctions utilized for performing a plurality of functions of the one ormore applications wherein a plurality of data models generated by acontroller performs the functions in real-time; at least one data modeldatabase configured for storing the plurality of data models, whereinthe data lake is configured to store re-calibrated or re-modelled datamodels associated with the one or more applications wherein the datamodels are re-calibrated based on a predicted impact of characteristicof at least one attribute of the stored data on the one or moreapplications.
 2. The data lake of claim 1, wherein the characteristic ofthe at least one attribute includes change in the at least one attributeor determination of the attribute as a new attribute; wherein aprocessor communicatively coupled to the data lake is configured to:identify a plurality of data models to generate an impact data forpredicting impact of the change on the one or more applications, inresponse to change in the at least one attribute wherein the pluralityof data models is auto-selected based on the change; create at least onescript by a bot based on the plurality of data models, the change in theat least one attribute, the impact data and AI based processing logicfor recommending an action/task wherein a plurality of functions of theone or more applications are re-calibrated automatically in real-timebased on the recommended action/task; determine the one or moreapplications utilizing the data from the distinct sources, in responseto determination of the attribute as a new attribute; and re-calibratingor remodeling a plurality of data models associated with the one or moreapplications based on the new attribute wherein the plurality of datamodels is re-calibrated based on predicted impact of the new attributeon the one or more applications; wherein an AI based prediction andrecommendation engine is configured to recommend the action/tasks. 3.The data lake of claim 2, wherein the plurality of distinct sourcesincludes internet of things (IOT) device, demand from various sources atdifferent levels like retailers, distribution channels, POS systems,customer feedback, supplier collaboration platform, invoices, purchaseorders (PO), finance modules, inventory management module, contracts andRFx module, supplier module, item master, bill of materials, vendormaster, warehouse management module, logistics management module, socialmedia, weather, real time commodity and stock market prices,geo-political news.
 4. The data lake of claim 2, wherein the one or moreapplications include enterprise applications (EA) and supply chainmanagement (SCM) applications.
 5. The data lake of claim 4, wherein theenterprise applications include finance applications like automatedbilling applications and payment processing applications, Customerrelationship management applications (CRM) and enterprise resourceplanning applications (ERP).
 6. The data lake of claim 5, wherein the EAand SCM applications include a plurality of nodes like inventory,logistics, warehouse, procurement, customers, supplier, retailers,distributors, resellers, co-packers and transportation wherein the nodesinteract with each other to structure the plurality of functionsassociated with the applications.
 7. The data lake of claim 1, whereinthe plurality of functions includes demand planning, supply planning,production planning, forecasting, smart factory and fulfillmentplanning.
 8. The data lake of claim 7, wherein the recommendedtask/action includes auto adjust data for the plurality of functions,risk mitigation, or direct interaction with the plurality of nodes. 9.The data lake of claim 1, further comprises a graph store with datarelations analytics for providing real-time recommendation based on ahistorical data and relations wherein the graph store utilizes a graphstore library to detect complex patterns and structures in the datamodel.
 10. The data lake of claim 3, wherein the data models includepro-active detection algorithms for detecting any record/transactionsbeing entered by the user at a user interface, thereby ensuring aplurality of master tables of the data models are clean, accurate,complete and non-fraudulent/non-duplicate at any point in time and thedata flowing through the one or more applications is clean and accurate.11. The data lake of claim 10, wherein a data curator engine isconfigured to collect data from the distinct sources and act as agateway to identify the at least one data attribute from the receiveddata that is to be extracted as assessed from one or more applications.12. A system comprising: a data lake configured to store a plurality ofdata from distinct sources in real-time wherein the data lake includes aplurality of relational and non-relational databases configured forstoring a plurality of structured and unstructured data received fromdistinct sources in real-time; at least one functional database storinga library of functions utilized for performing a plurality of functionsof the one or more applications wherein a plurality of data modelsgenerated by a controller performs the functions in real-time; at leastone data model database configured for storing the plurality of datamodels, wherein the data lake is configured to store re-calibrated orre-modelled data models associated with the one or more applicationswherein the data models are re-calibrated based on a predicted impact ofcharacteristic of at least one attribute of the stored data on the oneor more applications wherein the characteristic of the at least oneattribute includes change in the at least one attribute or determinationof the attribute as a new attribute; a control tower configured forcontrolling a plurality of functions associated with the one or moreapplications wherein the control tower determines characteristic of atleast one attribute of one or more of the plurality of received datawherein characteristic includes change in the at least one attribute ordetermination of the attribute as a new attribute; a controller encodedwith instructions enabling the controller to function as a bot togenerate a plurality of data models created for performing the pluralityof functions by utilizing a library of functions stored on a functionaldatabase of the data lake; and an AI based prediction and recommendationengine coupled to a processor configured for processing at least oneprediction algorithm to generate at least one recommendation option inreal time, wherein the processor communicatively coupled to the datalake is configured to: identify the plurality of data models to generatean impact data for predicting impact of the change on the one or moreapplications, in response to change in the at least one attributewherein the plurality of data models is auto-selected based on thechange; create at least one script by a bot based on the plurality ofdata models, the change in the at least one attribute, the impact dataand AI based processing logic for recommending an action/task wherein aplurality of functions of the one or more applications are re-calibratedautomatically in real-time based on the recommended action/task;determine the one or more applications utilizing the data from thedistinct sources, in response to determination of the attribute as a newattribute; and re-calibrating or remodeling the plurality of data modelsassociated with the one or more applications based on the new attributewherein the plurality of data models is re-calibrated based on predictedimpact of the new attribute on the one or more applications, wherein theAI based prediction and recommendation engine is configured to recommendthe action/tasks.
 13. The system of claim 12, wherein the impact data isdetermined by the data models for predicting impact of the change on theone or more applications wherein the data models are auto-selected basedon the change.
 14. The system of claim 13, wherein the TOT data includessensor data on plant machinery, logistics carriers, security systems,warehouse cameras and sensors etc.
 15. The system of claim 14, whereinthe system is provided in a cloud or cloud-based computing environment.16. The system of claim 14, wherein the data is a text data, a voicedata, an image data or a combination thereof
 17. The system of claim 16,further comprises a test module configured to test the plurality of datamodels and apply the tested data models to the one or more applicationsin real time wherein the tested models enables real time switchingbetween the plurality of data models for different applications based onthe functions and efficiency of the tested data models wherein theswitching occurs in real time using AI based analysis of a performancedata of the data models, the received data, and the functions related tothe one or more applications.
 18. The system of claim 17, wherein theone or more applications include enterprise applications (EA) and supplychain management (SCM) applications wherein the EA and SCM applicationsinclude a plurality of nodes like inventory, logistics, warehouse,procurement, customers, supplier, retailers, distributors, resellers,co-packers and transportation wherein the nodes interact with each otherto structure the plurality of functions associated with theapplications.
 19. The system of claim 18, wherein the recommendedtask/action includes auto adjust data for the plurality of functions,risk mitigation, or direct interaction with the plurality of nodes. 20.The system of claim 19, further comprises interconnected data across theplurality of functions connecting demand with supply by combiningcustomer and supplier data in real time and thereby structuring acollaborative platform for multiple entities like supplier, customer,factories and warehouses.
 21. A computer program product for operatingone or more application of a computing device with memory, the productcomprising: a computer readable storage medium readable by a processorand storing instructions for execution by the processor for performing amethod, the method comprising: storing a plurality of data from distinctdata sources in a data lake in real time, wherein the data lakeincludes: a plurality of relational and non-relational databasesconfigured for storing a plurality of structured and unstructured datareceived from distinct sources in real-time; at least one functionaldatabase storing a library of functions utilized for performing aplurality of functions of the one or more applications wherein aplurality of data models generated by a controller performs thefunctions in real-time; at least one data model database configured forstoring the plurality of data models, wherein the data lake isconfigured to store re-calibrated or re-modelled data models associatedwith the one or more applications wherein the data models arere-calibrated based on a predicted impact of characteristic of at leastone attribute of the stored data on the one or more applications whereinthe characteristic of the at least one attribute includes change in theat least one attribute or determination of the attribute as a newattribute; identifying the plurality of data models to generate animpact data for predicting impact of change on the one or moreapplications, in response to change in at least one attribute whereinthe plurality of data models is auto-selected based on the change;creating at least one script by a bot based on the plurality of datamodels, the change in the at least one attribute, the impact data and AIbased processing logic for recommending an action/task wherein aplurality of functions of the one or more applications are re-calibratedautomatically in real-time based on the recommended action/task;determining the one or more applications utilizing the data from thedistinct sources, in response to determination of the attribute as a newattribute; and re-calibrating or remodeling the plurality of data modelsassociated with the one or more applications based on the new attributewherein the plurality of data models is re-calibrated based on predictedimpact of the new attribute on the one or more applications, wherein anAI based prediction and recommendation engine is configured to recommendthe action/tasks.